Patents by Inventor Nanzhu Wang
Nanzhu Wang has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240104122Abstract: Implementations relate to updating agricultural records that include inferences gathered by one or more sensors deployed at an agricultural location. The records are matched to mapping tiles of a mapping application and the mapping tile records are updated according to the updated data that was received. Implementations further include identifying parent tiles to each of the updated records, where a parent tile is an aggregation of multiple mapping tiles that can be utilized by a mapping application to render an interface that allows a user to view the data with varying degrees of granularity.Type: ApplicationFiled: September 28, 2022Publication date: March 28, 2024Inventors: Nanzhu Wang, Hong Wu, Jie Gu
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Publication number: 20240094735Abstract: Techniques are described herein for an interactive user interface for feature engineering and data processing on geospatial maps. A method includes: receiving user input indicating a selection of a first agricultural data source; receiving user input indicating a geometry on a map, the geometry defining a geographic region on which to perform at least one operation; receiving user input defining the at least one operation to be performed on the geographic region; generating an agricultural analysis request based on the first agricultural data source, the geometry, and the the at least one operation to be performed on the geographic region; decoding the agricultural analysis request to generate an operation tree including a plurality of operation nodes; executing each of the operation nodes to generate an agricultural analysis result corresponding to the geographic region; and displaying a visual representation based on the agricultural analysis result corresponding to the geographic region.Type: ApplicationFiled: September 16, 2022Publication date: March 21, 2024Inventor: Nanzhu Wang
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Publication number: 20230288225Abstract: Implementations are directed to assigning corresponding semantic identifiers to a plurality of rows of an agricultural field, generating a local mapping of the agricultural field that includes the plurality of rows of the agricultural field, and subsequently utilizing the local mapping in performance of one or more agricultural operations. In some implementations, the local mapping can be generated based on overhead vision data that captures at least a portion of the agricultural field. In these implementations, the local mapping can be generated based on GPS data associated with the portion of the agricultural field captured in the overhead vision data. In other implementations, the local mapping can be generated based on driving data generated during an episode of locomotion of a vehicle through the agricultural field. In these implementations, the local mapping can be generated based on GPS data associated with the vehicle traversing through the agricultural field.Type: ApplicationFiled: May 18, 2023Publication date: September 14, 2023Inventors: Alan Eneev, Jie Yang, Yueqi Li, Yujing Qian, Nanzhu Wang, Sicong Wang, Sergey Yaroshenko
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Patent number: 11734511Abstract: Techniques are disclosed that enable generating a unified data set by mapping a set of item description phrases, describing entries in a data set, to a set of canonical phrases. Various implementations include generating a similarity measure between each item description phrase and each canonical phrase by processing the corresponding item description phrase and the corresponding canonical phrase using a natural language processing model. Additional or alternative implementations include generating a bipartite graph based on the set of item description phrases, the set of canonical phrases, and the similarity measures. The mapping can be generated based on the bipartite graph.Type: GrantFiled: July 8, 2020Date of Patent: August 22, 2023Assignee: MINERAL EARTH SCIENCES LLCInventors: Nanzhu Wang, Gaoxiang Chen, Yueqi Li
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Patent number: 11709860Abstract: Some implementations herein relate to a graphical user interface (GUI) that facilitates dynamically partitioning agricultural fields into clusters on an individual agricultural field-basis using agricultural features. A map of a geographic area containing a plurality of agricultural fields may be rendered as part of a GUI. The agricultural fields may be partitioned into a first set of clusters based on a first granularity value and agricultural features of individual agricultural fields. The individual agricultural fields may be visually annotated in the GUI to convey the first set of clusters of similar agricultural fields. Upon receipt of a second granularity value different from the first granularity value, the agricultural fields may be partitioned into a second set of clusters of similar agricultural fields. The map of the geographic area may be updated so that individual agricultural fields are visually annotated to convey the second set of clusters.Type: GrantFiled: March 28, 2022Date of Patent: July 25, 2023Assignee: MINERAL EARTH SCIENCES LLCInventors: David Clifford, Ming Zheng, Elliott Grant, Nanzhu Wang, Cheng-en Guo, Aleksandra Deis
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Patent number: 11703351Abstract: Implementations are directed to assigning corresponding semantic identifiers to a plurality of rows of an agricultural field, generating a local mapping of the agricultural field that includes the plurality of rows of the agricultural field, and subsequently utilizing the local mapping in performance of one or more agricultural operations. In some implementations, the local mapping can be generated based on overhead vision data that captures at least a portion of the agricultural field. In these implementations, the local mapping can be generated based on GPS data associated with the portion of the agricultural field captured in the overhead vision data. In other implementations, the local mapping can be generated based on driving data generated during an episode of locomotion of a vehicle through the agricultural field. In these implementations, the local mapping can be generated based on GPS data associated with the vehicle traversing through the agricultural field.Type: GrantFiled: December 22, 2020Date of Patent: July 18, 2023Assignee: MINERAL EARTH SCIENCES LLCInventors: Alan Eneev, Jie Yang, Yueqi Li, Yujing Qian, Nanzhu Wang, Sicong Wang, Sergey Yaroshenko
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Patent number: 11687960Abstract: Implementations are described herein for using machine learning to determine whether candidate crop fields are suitable for management by particular agricultural entities. In various implementations, a machine learning model may be applied to input data to generate output data. The input data may include a first plurality of data points corresponding to field-level agricultural management practices of an agricultural entity. The output data may be indicative of one or more predicted outcomes of the agricultural entity implementing the field-level agricultural management practices on one or more candidate crop fields not currently managed by the agricultural entity. Based on one or more of the predicted outcomes, one or more computing devices may be caused to provide a user associated with the agricultural entity with information about one or more of the candidate crop fields, and/or one or more parameter inputs of a graphical user interface may be prepopulated.Type: GrantFiled: March 8, 2022Date of Patent: June 27, 2023Assignee: MINERAL EARTH SCIENCES LLCInventors: Nanzhu Wang, Chunfeng Wen, Yueqi Li
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Publication number: 20220215037Abstract: Some implementations herein relate to a graphical user interface (GUI) that facilitates dynamically partitioning agricultural fields into clusters on an individual agricultural field-basis using agricultural features. A map of a geographic area containing a plurality of agricultural fields may be rendered as part of a GUI. The agricultural fields may be partitioned into a first set of clusters based on a first granularity value and agricultural features of individual agricultural fields. The individual agricultural fields may be visually annotated in the GUI to convey the first set of clusters of similar agricultural fields. Upon receipt of a second granularity value different from the first granularity value, the agricultural fields may be partitioned into a second set of clusters of similar agricultural fields. The map of the geographic area may be updated so that individual agricultural fields are visually annotated to convey the second set of clusters.Type: ApplicationFiled: March 28, 2022Publication date: July 7, 2022Inventors: David Clifford, Ming Zheng, Elliott Grant, Nanzhu Wang, Cheng-en Guo, Aleksandra Deis
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Publication number: 20220196433Abstract: Implementations are directed to assigning corresponding semantic identifiers to a plurality of rows of an agricultural field, generating a local mapping of the agricultural field that includes the plurality of rows of the agricultural field, and subsequently utilizing the local mapping in performance of one or more agricultural operations. In some implementations, the local mapping can be generated based on overhead vision data that captures at least a portion of the agricultural field. In these implementations, the local mapping can be generated based on GPS data associated with the portion of the agricultural field captured in the overhead vision data. In other implementations, the local mapping can be generated based on driving data generated during an episode of locomotion of a vehicle through the agricultural field. In these implementations, the local mapping can be generated based on GPS data associated with the vehicle traversing through the agricultural field.Type: ApplicationFiled: December 22, 2020Publication date: June 23, 2022Inventors: Alan Eneev, Jie Yang, Yueqi Li, Yujing Qian, Nanzhu Wang, Sicong Wang, Sergey Yaroshenko
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Publication number: 20220188854Abstract: Implementations are described herein for using machine learning to determine whether candidate crop fields are suitable for management by particular agricultural entities. In various implementations, a machine learning model may be applied to input data to generate output data. The input data may include a first plurality of data points corresponding to field-level agricultural management practices of an agricultural entity. The output data may be indicative of one or more predicted outcomes of the agricultural entity implementing the field-level agricultural management practices on one or more candidate crop fields not currently managed by the agricultural entity. Based on one or more of the predicted outcomes, one or more computing devices may be caused to provide a user associated with the agricultural entity with information about one or more of the candidate crop fields, and/or one or more parameter inputs of a graphical user interface may be prepopulated.Type: ApplicationFiled: March 8, 2022Publication date: June 16, 2022Inventors: Nanzhu Wang, Chunfeng Wen, Yueqi Li
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Patent number: 11321347Abstract: Some implementations herein relate to a graphical user interface (GUI) that facilitates dynamically partitioning agricultural fields into clusters on an individual agricultural field-basis using agricultural features. A map of a geographic area containing a plurality of agricultural fields may be rendered as part of a GUI. The agricultural fields may be partitioned into a first set of clusters based on a first granularity value and agricultural features of individual agricultural fields. The individual agricultural fields may be visually annotated in the GUI to convey the first set of clusters of similar agricultural fields. Upon receipt of a second granularity value different from the first granularity value, the agricultural fields may be partitioned into a second set of clusters of similar agricultural fields. The map of the geographic area may be updated so that individual agricultural fields are visually annotated to convey the second set of clusters.Type: GrantFiled: October 20, 2020Date of Patent: May 3, 2022Assignee: X DEVELOPMENT LLCInventors: David Clifford, Ming Zheng, Elliott Grant, Nanzhu Wang, Cheng-en Guo, Aleksandra Deis
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Publication number: 20220122298Abstract: Some implementations herein relate to a graphical user interface (GUI) that facilitates dynamically partitioning agricultural fields into clusters on an individual agricultural field-basis using agricultural features. A map of a geographic area containing a plurality of agricultural fields may be rendered as part of a GUI. The agricultural fields may be partitioned into a first set of clusters based on a first granularity value and agricultural features of individual agricultural fields. The individual agricultural fields may be visually annotated in the GUI to convey the first set of clusters of similar agricultural fields. Upon receipt of a second granularity value different from the first granularity value, the agricultural fields may be partitioned into a second set of clusters of similar agricultural fields. The map of the geographic area may be updated so that individual agricultural fields are visually annotated to convey the second set of clusters.Type: ApplicationFiled: October 20, 2020Publication date: April 21, 2022Inventors: David Clifford, Ming Zheng, Elliott Grant, Nanzhu Wang, Cheng-en Guo, Aleksandra Deis
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Patent number: 11295331Abstract: Implementations are described herein for using machine learning to determine whether candidate crop fields are suitable for management by particular agricultural entities. In various implementations, a machine learning model may be applied to input data to generate output data. The input data may include a first plurality of data points corresponding to field-level agricultural management practices of an agricultural entity. The output data may be indicative of one or more predicted outcomes of the agricultural entity implementing the field-level agricultural management practices on one or more candidate crop fields not currently managed by the agricultural entity. Based on one or more of the predicted outcomes, one or more computing devices may be caused to provide a user associated with the agricultural entity with information about one or more of the candidate crop fields, and/or one or more parameter inputs of a graphical user interface may be prepopulated.Type: GrantFiled: July 1, 2020Date of Patent: April 5, 2022Assignee: X DEVELOPMENT LLCInventors: Nanzhu Wang, Chunfeng Wen, Yueqi Li
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Publication number: 20220005055Abstract: Implementations are described herein for using machine learning to determine whether candidate crop fields are suitable for management by particular agricultural entities. In various implementations, a machine learning model may be applied to input data to generate output data. The input data may include a first plurality of data points corresponding to field-level agricultural management practices of an agricultural entity. The output data may be indicative of one or more predicted outcomes of the agricultural entity implementing the field-level agricultural management practices on one or more candidate crop fields not currently managed by the agricultural entity. Based on one or more of the predicted outcomes, one or more computing devices may be caused to provide a user associated with the agricultural entity with information about one or more of the candidate crop fields, and/or one or more parameter inputs of a graphical user interface may be prepopulated.Type: ApplicationFiled: July 1, 2020Publication date: January 6, 2022Inventors: Nanzhu Wang, Chunfeng Wen, Yueqi Li